Prototype System Research Articles

Virtual-real mapping for the scraper conveyor S-shaped bending in a fully mechanized mining face production system based on digital twin

ABSTRACT The human-centered paradigm of intelligent manufacturing is reshaping traditional production models. The fully mechanized coal mining face production system (FMCMFPS) represents a typical application of complex manufacturing systems (MS) in the coal mining industry, with an increased emphasis on the safety of personnel within the system. Among its key components, the real-time monitoring of the scraper conveyor S-shaped bending is critical to ensuring safe and efficient coal mining operations. This monitoring also aligns with the principles of Industry 5.0, which emphasize human-centric, safe and intelligent manufacturing practices. However, the underground environment is harsh, with moisture, dust and high vibrations. These conditions, along with the structure of the scraper conveyor, complicate the accurate capture of data. Issues such as data misalignment, missing information and frame retraction errors lead to inconsistencies between sensor data and actual conditions. To tackle these challenges, the paper presents a virtual-real mapping method for the scraper conveyor S-shaped bending using digital twin technology. A framework and mechanistic model for the S-shaped bending are established, and techniques like the Interquartile Range (IQR), sliding window and filtering algorithms are used to handle anomalous data. The proposed approach is validated through a prototype system at the Ulanmulun Mine in Inner Mongolia.

Prototipe Monitoring dan Controlling Lampu Ruangan Bersekat Kaca via Bluetooth Menggunakan Smartphone

Smart home technology continues to evolve, offering convenience and ease in managing electronic devices. One essential aspect of smart home systems is lighting control. This study aims to design and implement a prototype system for monitoring and controlling room lights separated by glass partitions using Bluetooth technology integrated with a smartphone. The research methodology includes hardware design using an Arduino Uno microcontroller, HC-05 Bluetooth module, and supporting sensors, as well as the development of an Android application as the user interface. The system is designed to control lights wirelessly, even in rooms with glass partitions. Tests were conducted to evaluate the reliability of the Bluetooth connection, system responsiveness, and ease of use of the application. The results show that the prototype successfully controlled lights within an optimal range of up to 10 meters, even with glass partitions. The developed smartphone application could display the real-time status of the lights and allowed users to switch them on or off with a response time of less than one second.in conclusion, this prototype provides an effective solution for lighting control in homes or workspaces, enhancing energy efficiency and offering greater convenience for users. This technology has the potential to be widely adopted in cost-effective and user-friendly smart home systems.

Technique selection and technical developments for 2D dual-energy subtraction angiography on an interventional C-arm.

Dual-energy (DE) x-ray image acquisition has the potential to provide material-specific angiographic images in the interventional suite. This approach can be implemented with novel detector technologies, such as dual-layer and photon-counting detectors. Alternatively, DE imaging can be implemented on existing systems using fast kV-switching. Currently, there are no commercially available DE options for interventional platforms. This study reports on the development of a prototype fast kV-switching DE subtraction angiography system. In contrast to alternative approaches to DE imaging in the interventional suite, this prototype uses a clinically available interventional C-arm equipped with special x-ray tube control software. An automatic exposure control algorithm and technical features needed for such a system in the interventional setting are developed and validated in phantom studies. Fast kV-switching was implemented on an interventional C-arm platform using software that enables frame-by-frame specification of x-ray tube techniques (e.g., tube voltage/kV, pulse width/ms, tube current/mA). A real-time image display was developed on a portable workstation to display DE subtraction images in real-time (nominal 15 frame/s). An empirical CNR-driven automatic exposure control (AEC) algorithm was created to guide DE tube technique selection (kV pair, ms pair, mA). The AEC model contained a look-up table which related DE tube technique parameters and air kerma to iodine CNR, which was measured in acrylic phantom models containing an iodine-equivalent reference object. For a given iodine CNR request, the AEC algorithm estimated patient thickness and then selected the DE tube technique expected to deliver the requested CNR at the minimum air kerma. The AEC algorithm was developed for DE imaging performed without and with the application of anti-correlated noise reduction (ACNR). Validation of the AEC model was performed by comparing the AEC-predicted iodine CNR values with directly measured values in a separate phantom study. Both dose efficiency (CNR2/kerma) and maximum achievable iodine CNR (within tube technique constraints) were quantified. Finally, improvements in DE iodine CNR were quantified using a novel variant to the ACNR approach, which used machine-learning image denoising (ACNR-ML). The prototype system provided a continuous display of DE subtraction images. For standard DE imaging, the AEC-predicted iodine CNR values agreed with directly measured values to within 3.5%±1.6% (mean±standard deviation). When ACNR was applied, predicted iodine CNR agreed with measurement to within 2.1%±3.3%. AEC-generated DE techniques were typically (low/high energy): 63/125kV, 10/3.2ms, with varying mA values. When ACNR was applied, dose efficiency was increased by a factor of 9.37±2.08 and maximum CNR was increased by a factor of 3.29±0.21 relative to DE without denoising. Application of ACNR-ML yielded a greater increase in both the dose efficiency (16.11±2.99) and maximum CNR (4.46±0.31) compared to DE without denoising. A prototype DE subtraction angiography system using fast kV-switching was implemented on a clinically available interventional C-arm platform without modification of system hardware. The technical features presented in this work include a real-time image display, noise-reduction strategies, and a CNR-driven AEC algorithm. This prototype system demonstrates the feasibility of 2D dual-energy imaging for image-guided interventions.

Constant pH Simulation with FMM Electrostatics in GROMACS. (A) Design and Applications.

The structural dynamics of biological macromolecules, such as proteins, DNA/RNA, or complexes thereof, are strongly influenced by protonation changes of their typically many titratable groups, which explains their sensitivity to pH changes. Conversely, conformational and environmental changes of the biomolecule affect the protonation state of these groups. With few exceptions, conventional force field-based molecular dynamics (MD) simulations neither account for these effects nor do they allow for coupling to a pH buffer. Here, we present design decisions and applications of a rigorous Hamiltonian interpolation λ-dynamics constant pH method in GROMACS, which rests on GPU-accelerated Fast Multipole Method (FMM) electrostatics. Our implementation supports both CHARMM36m and Amber99sb*-ILDN force fields and is largely automated to enable seamless switching from regular MD to constant pH MD, involving minimal changes to the input files. Here, the first of two companion papers describes the underlying constant pH protocol and sample applications to several prototypical benchmark systems such as cardiotoxin V, lysozyme, and staphylococcal nuclease. Enhanced convergence is achieved through a new dynamic barrier height optimization method, and high pKa accuracy is demonstrated. We use Functional Mode Analysis (FMA) and Mutual Information (MI) to explore the complex intra- and intermolecular couplings between the protonation states of titratable groups as well as those between protonation states and conformational dynamics. We identify striking conformation-dependent pKa variations and unexpected inter-residue couplings. Conformation-protonation coupling is identified as a primary cause of the slow protonation convergence notorious to constant pH simulations involving multiple titratable groups, suggesting enhanced sampling methods to accelerate convergence.

Mapping and Summarizing the Research on AI Systems for Automating Medical History Taking and Triage: Scoping Review.

The integration of artificial intelligence (AI) systems for automating medical history taking and triage can significantly enhance patient flow in health care systems. Despite the promising performance of numerous AI studies, only a limited number of these systems have been successfully integrated into routine health care practice. To elucidate how AI systems can create value in this context, it is crucial to identify the current state of knowledge, including the readiness of these systems, the facilitators of and barriers to their implementation, and the perspectives of various stakeholders involved in their development and deployment. This study aims to map and summarize empirical research on AI systems designed for automating medical history taking and triage in health care settings. The study was conducted following the framework proposed by Arksey and O'Malley and adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) guidelines. A comprehensive search of 5 databases-PubMed, CINAHL, PsycINFO, Scopus, and Web of Science-was performed. A detailed protocol was established before the review to ensure methodological rigor. A total of 1248 research publications were identified and screened. Of these, 86 (6.89%) met the eligibility criteria. Notably, most (n=63, 73%) studies were published between 2020 and 2022, with a significant concentration on emergency care (n=32, 37%). Other clinical contexts included radiology (n=12, 14%) and primary care (n=6, 7%). Many (n=15, 17%) studies did not specify a clinical context. Most (n=31, 36%) studies used retrospective designs, while others (n=34, 40%) did not specify their methodologies. The predominant type of AI system identified was the hybrid model (n=68, 79%), with forecasting (n=40, 47%) and recognition (n=36, 42%) being the most common tasks performed. While most (n=70, 81%) studies included patient populations, only 1 (1%) study investigated patients' views on AI-based medical history taking and triage, and 2 (2%) studies considered health care professionals' perspectives. Furthermore, only 6 (7%) studies validated or demonstrated AI systems in relevant clinical settings through real-time model testing, workflow implementation, clinical outcome evaluation, or integration into practice. Most (n=76, 88%) studies were concerned with the prototyping, development, or validation of AI systems. In total, 4 (5%) studies were reviews of several empirical studies conducted in different clinical settings. The facilitators and barriers to AI system implementation were categorized into 4 themes: technical aspects, contextual and cultural considerations, end-user engagement, and evaluation processes. This review highlights current trends, stakeholder perspectives, stages of innovation development, and key influencing factors related to implementing AI systems in health care. The identified literature gaps regarding stakeholder perspectives and the limited research on AI systems for automating medical history taking and triage indicate significant opportunities for further investigation and development in this evolving field.

Torque Measurement System Design for Step Motor 23HS30-3004S

This article describes the methodology employed in the development of a prototype system designed for torque measurement. The system consists of two components. Α rotating component, the transmitter, and a fixed component, the receiver. The torque data, obtained from a strain gauge bridge, are processed and transmitted to the receiver through a transformer-operated device. Rotary transformers are deployed to supply power to electronics that are mounted on shafts. This ensures continuous and stable data transmission without requiring physical connections. Additionally, the accuracy of the design model is verified through design equations and simulation results, reinforcing the system’s feasibility and reliability for real-world applications.

Contact ion-pair SN2 reactions activated by Lewis Base Phase transfer catalysts

We present experimental probes of contact ion-pair (CIP) SN2 reactions for simplest prototype systems by 19F-NMR spectroscopy. This study provides crucial evidences for the reactions of CIP metal salts facilitated by Lewis base phase transfer catalysts (PTCs) [2,2,2]-cryptand, 18-crown-6, pentaethylene glycols (pentaEGs) and BINOL–based pentaEG. The 19F-NMR spectra of MF (M = K, Cs) ion-pairs in various solvents are used as fingerprinting tools to identify the CIP CsF/PTC/substrate complexes in SN2 reactions. Examination of the prototype reactions demonstrates that the novel CIP mechanism, which is in sharp contrast to the conventional perspectives, may account for the observed phenomenal efficiency of the SN2 processes using the alkali metal salts under the influence of various PTCs. In this extremely efficient and selective protocol of wide applicability, the CIP MF is activated by the Lewis base PTCs acting on the counter-cation M+ to drastically mitigate the latter’s retarding Coulomb forces on the adjacent nucleophile F-, with or without the participation of hydrogen bonds.

Structural Modulation and Enhanced Magnetic Ordering in Incommensurate K1-xCrSe2 Crystals.

Layered delafossite-type compounds and related transition metal dichalcogenides, characterized by their triangular net structures, serve as prototypical systems for exploring the intricate interplay between crystal structure and magnetic behavior. Herein, we report on the discovery of the compound K1-xCrSe2 (x ≈ 0.13), an incommensurately modulated phase. Single crystals of this compound were grown for the first time using a K/Se self-flux. We find a monoclinic crystal structure with incommensurate modulation that can be rationalized by a 3 + 1-dimensional model. This modulation compensates for the under-stoichiometry of K cations, creating pronounced undulations in the CrSe2 layers. Our anisotropic magnetization measurements reveal that K1-xCrSe2 undergoes a transition to a long-range magnetically ordered state below TN = 133 K, a temperature 1.6 to 3.3 times higher than in earlier reported KCrSe2 compounds. Our findings open new avenues for tuning the magnetic properties of these layered materials through structural modulation.

Design and Development of a Cost Effective WebVR Commerce System Prototype

This project focuses on the design and development of a cost-effective WebVR commerce system prototype to enhance the online shopping experience. Despite the rapid growth of E-commerce, platforms face challenges such as limited interactivity, static product presentations, and lengthy purchasing procedures, which often lead customers to abandon online shopping in favor of physical stores. The proposed WebVR system leverages virtual reality to address these issues, providing an immersive and interactive shopping experience. By enabling users to visualize and explore products in a 3D virtual environment, the system bridges the gap between online and in-store shopping. However, virtual reality often requires costly hardware and powerful workstations to process the positioning data. Therefore, this project focuses on utilizing affordable technologies and frameworks to create a seamless and accessible solution, making WebVR a more practical option for a wider audience.

High-speed spectral imaging via multispectral pulse illumination and temporal-spectral decoupling.

Spectral imaging (SI) faces inherent trade-offs among spatial, temporal, and spectral resolutions due to limited bandwidth. Current high-speed SI systems, relying on fast modulation or specific cameras, compromise spatial resolution or suffer high complexity and cost. This work presents a low-cost, high-speed SI scheme achieving up to 240 frames per second (fps) and even μs-resolved multispectral observation. This work reveals the inherent temporal-spectral redundancy in SI videos and reports an active temporal-spectral coupling (TSC) and decoupling (TSD) strategy for high-speed SI. We established a prototype system using a snapshot SI camera and a multispectral LED array. The LEDs sequentially illuminate the scene channel by channel in one exposure for TSC acquisition. Each channel of the measurement corresponds one-to-one to an illumination moment. A transformer-based network is applied to decouple textural and spectral information from the measurement and re-couple them to reconstruct images across all channels and time instances. Experiments validated that the reported framework can successfully record μs-resolved multispectral videos.

Car Rental System

This Paper gives a quick summary of web based car rental system , where you can rent a car from this website. Customer can rent what ever car they want for a short period of time . It is safe and secure website cause we use some verification process example: information of aadhar card number and driver licence to ensure the driver who is driving the car is above 18 age and has driver licence . this website include the feature like customer login ,password ,check availability od vehicle ,and direct chat with the admin, make payment through credit or debit card .for admin the features are admin login , adding cars ,deleting the cars ,accept or reject the request from the customer. This set of less time consuming and slightly secured when compared to the traditional method ,but still requires the admin to manually key in all client information .This study are develop the vehicles to rent online which include cars, motorcycles and vans and etc. to enhance the coverage of online vehicles rental services in Nashik and finally is to improve the online system for increasing the brands and models over hales for cars and motorcycles and vans to rent online Nashik .other companies are trying to revolutionize their business to adapt the mobile technology that been growing rapid in recent years .Therefore in the work the project aimed to propose a mobile car rental system prototype that is secured and allowed the cars to make reservation of the vehicle that desired. The mobile car rental system includes features as register and login page ,list of vehicles and database related to mobile car rental system

Opportunities for gas-phase science at short-wavelength free-electron lasers with undulator-based polarization control

Free-electron lasers (FELs) are the world's most brilliant light sources with rapidly evolving technological capabilities in terms of ultrabright and ultrashort pulses over a large range of photon energies. Their revolutionary and innovative developments have opened new fields of science regarding nonlinear light-matter interaction, the investigation of ultrafast processes from specific observer sites, and approaches to imaging matter with atomic resolution. A core aspect of FEL science is the study of isolated and prototypical systems in the gas phase with the possibility of addressing well-defined electronic transitions or particular atomic sites in molecules. Notably for polarization-controlled short-wavelength FELs, the gas phase offers new avenues for investigations of nonlinear and ultrafast phenomena in spin-orientated systems, for decoding the function of the chiral building blocks of life as well as steering reactions and particle emission dynamics in otherwise inaccessible ways. This roadmap comprises descriptions of technological capabilities of facilities worldwide, innovative diagnostics and instrumentation, as well as recent scientific highlights, novel methodology, and mathematical modeling. The experimental and theoretical landscape of using polarization controllable FELs for dichroic light-matter interaction in the gas phase will be discussed and comprehensively outlined to stimulate and strengthen global collaborative efforts of all disciplines. Published by the American Physical Society 2025

Knowledge-driven semantic converting method of multimodal models toward a geospatial perspective

ABSTRACT In the virtual geographic environment, conducting status analysis on urban structures and similar objects is crucial for enhancing their detailed management level. However, it is challenging to directly convert the same object across various software systems with different modalities (such as spatial analysis, BIM design, numerical simulation etc.). Therefore, the effective conversion of multi-modal models becomes pivotal. Due to the characteristics of inconsistent spatial description and complex association relationship among multimodal models, resulting in low knowledge reuse rate, poor accuracy of unit mapping, and low efficiency of state sharing in the process of model conversion. Aiming at these problems, this article delves into the knowledge-driven semantic conversion techniques for multi-modal models from a geospatial viewpoint. The mapping relationships between multimodal models in terms of spatial, geometric, and semantic information were first clarified. Subsequently, a structural matching template based on knowledge reuse was established, and a knowledge-guided algorithm for multimodal model transformation was designed. Finally, using a suspension bridge as a case study, a prototype system was developed and experimental analysis was conducted. The experimental results show that the method proposed in this article can accurately convert between BIM models, numerical analysis models, and GIS scene models, with spatial coordinate accuracy controlled within 1 mm and a conversion efficiency increase of more than 10 times. This can effectively enhance the integrated performance of models in applications such as digital geospatial twin scenarios.

Mining population opinion about local police

Abstract Sentiment analysis, or opinion mining, is an important task of natural language processing (NLP) that extracts opinions, attitudes, and emotions from text. With the growth of digital platforms like blogs and social networks, opinion mining has become a key tool for organizations to understand public sentiment. In recent research, machine learning and lexicon-based approaches have been applied to analyze sentiments. Our work specifically focuses on national security, where sentiment analysis offers crucial insights into local opinions, helping authorities gauge public mood. As part of our research, we developed the Public Sensing about Police Platform, a prototype system designed to analyze emotions from social networks. This system generates dashboards for law enforcement and security agencies, providing actionable intelligence for public safety. Our findings show that “Hate” was the most common emotion expressed in relation to police interventions, indicating widespread unpopularity of these actions and a resulting sense of insecurity among the public.

Intelligent Upgrading of the Localized GNSS Monitoring System: Profound Integration of Blockchain and AI

With the extensive application of the Global Navigation Satellite System (GNSS), the intelligent upgrading of the GNSS monitoring system is of particular significance. Traditional GNSS monitoring systems typically rely on a centralized architecture, which possesses certain drawbacks when it comes to data tampering, fault tolerance, and data sharing. This paper presents an intelligently upgraded localized GNSS monitoring system that integrates blockchain and artificial intelligence (AI) technology to achieve the deep integration of security, transparency, and intelligent processing of monitoring data. Firstly, this paper employs blockchain technology to guarantee the integrity and tamper-resistance of GNSS monitoring data and utilizes a distributed ledger structure to realize the decentralization of data storage and transmission, thereby enhancing the anti-attack capability and reliability of the system. Secondly, the LSTM model is utilized to analyze and predict the vast amount of monitoring data in real-time, enabling the intelligent detection of GNSS signal anomalies and deviations and providing real-time early warnings to optimize the monitoring effect. Based on this architecture, we also combine the trained model with smart contracts to realize real-time monitoring and early warnings of GNSS satellites. By integrating the security guarantee of blockchain and the intelligent analysis ability of AI, the localized GNSS monitoring system can offer more efficient and accurate data monitoring and management services. In the study, we constructed a prototype system and tested it in both simulated and real environments. The results indicate that the system can effectively identify and respond to GNSS signal anomalies, and enhance the monitoring accuracy and response speed. Additionally, the application of blockchain enhances the immutability and traceability of data, providing a solid foundation for the long-term storage and auditing of GNSS data. The introduction of AI algorithms, especially the application of the Long Short-Term Memory (LSTM) network in anomaly detection, has significantly enhanced the system’s ability to recognize complex patterns.

Prototype of AI-powered assistance system for digitalisation of manual waste sorting.

Global waste generation is projected to reach 3.40 billion tons by 2050, necessitating improved waste sorting for effective recycling and progress toward a circular economy. Achieving this transformation requires higher sorting intensity through intensified processes, increased efficiency, and enhanced yield. While manual sorting remains common, smaller plants often use positive sorting to recover recyclables, and larger plants combine automated systems with manual sorting. Negative sorting is employed to remove impurities and improve material quality. However, innovation in manual sorting has stagnated. Advances in Machine Learning and Artificial Intelligence offer transformative potential for waste management, with digitalisation and improved recyclate quality becoming priorities. Despite these trends, manual sorting is still largely treated as a digital black box. The presented research outlines the design of a novel, human-centric AI-powered assistance system to support sorting workers by enhancing decision-making and real-time assistance during the sorting process, driving the digitalisation of manual sorting. Potential use cases, system requirements, and essential components were explored. High-quality use case-specific data is essential for model training. Therefore, publicly available datasets were evaluated but found inadequate, necessitating use-case-specific data acquisition through near-industry-scale experiments. This data was used to train and develop key system components, such as object recognition, classification, and action recognition models. Results indicate that transfer learning with a balanced dataset is effective for waste-sorting applications. The classification model achieved 81% accuracy on an experimental acquired balanced dataset, outperforming the accuracy of the pre-trained model on its original dataset.

Neural Density Functional Theory of Liquid-Gas Phase Coexistence

We use supervised machine learning together with the concepts of classical density functional theory to investigate the effects of interparticle attraction on the pair structure, thermodynamics, bulk liquid-gas coexistence, and associated interfacial phenomena in many-body systems. Local learning of the one-body direct correlation functional is based on Monte Carlo simulations of inhomogeneous systems with randomized thermodynamic conditions, randomized planar shapes of the external potential, and randomized box sizes. Focusing on the prototypical Lennard-Jones system, we test predictions of the resulting neural attractive density functional across a broad spectrum of physical behavior associated with liquid-gas phase coexistence in bulk and at interfaces. We analyze the bulk radial distribution function g(r) obtained from automatic differentiation and the Ornstein-Zernike route and determine (i) the Fisher-Widom line, i.e., the crossover of the asymptotic (large distance) decay of g(r) from monotonic to oscillatory, (ii) the (Widom) line of maximal correlation length, (iii) the line of maximal isothermal compressibility, and (iv) the spinodal by calculating the poles of the structure factor in the complex plane. The bulk binodal and the density profile of the free liquid-gas interface are obtained from density functional minimization and the corresponding surface tension from functional line integration. We also show that the neural functional describes accurately the phenomena of drying at a hard wall and of capillary evaporation for a liquid confined in a slit pore. Our neural framework yields results that improve significantly upon standard mean-field treatments of interparticle attraction. Comparison with independent simulation results demonstrates a consistent picture of phase separation even when restricting the training to supercritical states only. We argue that phase coexistence and its associated signatures can be discovered as emerging phenomena via functional mappings and educated extrapolation. Published by the American Physical Society 2025

A Methodology for Online Situational Awareness Provision in a Business Entity

This paper presents a practical approach to building situational awareness at a critical infrastructure entity and its implementation in supporting security management. It outlines the main factors for achieving a high level of situational awareness and ensuring the safety and continuity of business objectives. Among other things, situational awareness requires precise identification and understanding of multilateral dependencies of infrastructure, services, and processes executed by the entity and services provided by external businesses, resulting from the strong interrelationship of the critical infrastructure sectors and the sharing of resources. Accordingly, this paper presents a cyberspace modeling methodology that supports an in-depth analysis of the causes and consequences of threat proliferation in a complex infrastructure–business environment and its implementation in a Situational Awareness Management System (SAMS). An emphasis is placed on threat propagation analysis and dynamic risk assessment mechanisms and how they are used to identify and take preemptive actions protecting or limiting the scope of the threats’ propagation. The paper concludes with insights from a pilot implementation of the system prototype and directions for further work.

MINDPRES: A Hybrid Prototype System for Comprehensive Data Protection in the User Layer of the Mobile Cloud

MINDPRES: A Hybrid Prototype System for Comprehensive Data Protection in the User Layer of the Mobile Cloud

Innovative Capacitive Wireless Power System for Machines and Devices

This article deals with the design, development, and analysis of a wireless power transfer system prototype based on capacitive coupling. The system is intended for the continuous charging of a suspended mine drivetrain. It consists of a resonant inverter, primary and secondary matching circuits, a suitable capacitive coupler, and a rectifier with a load, ultimately serving as a battery charger for a mobile energy storage device. The system successfully achieved the target output voltage of 320 V and a charger output power of 2 kW at an operating frequency of 300 kHz. Additionally, the total system efficiency was at the level of 60%, ensuring that the RMS voltages on passive components remained below 3 kV.